1. Technical Field
The present disclosure relates to image processing, and more specifically, to generating reduced dataset images from hyperspectral images.
2. Introduction
A standard photograph or image is generated from the visible spectrum of light. A hyperspectral image is generated from a wider electromagnetic spectrum. For example, a hyperspectral image can include visible light as well as ultraviolet, infrared, or other forms of electromagnetic radiation. Thus, a hyperspectral image includes far more data than a standard image.
Space-borne hyperspectral imagers collect enough information to identify materials and substances on the ground. Scientists often use hyperspectral data to investigate land use, mineral deposits, or signs of climate change. However, the same data is also useful during disasters or other emergencies, when detection and mapping of fires, chemical agents, or flooded areas can provide critical information to first-responders, each of which relies on the ability to identify materials quickly and accurately.
Typically only a small portion of a hyperspectral image is useful to identify any given material. The sheer volume of data in hyperspectral images causes many material classification programs to run slowly and produce poor results, as they search the full image dataset for the information they need. Time-sensitive applications as well as applications which are not time-sensitive would benefit greatly from enhanced performance when analyzing hyperspectral images.